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--- |
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language: |
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- id |
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inference: false |
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tags: |
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- BERT |
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- HPLT |
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- encoder |
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license: apache-2.0 |
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datasets: |
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- HPLT/hplt_monolingual_v1_2 |
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--- |
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# HPLT Bert for Indonesian |
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<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%> |
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This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/). |
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It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/). |
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A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total). |
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All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup: |
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- hidden size: 768 |
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- attention heads: 12 |
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- layers: 12 |
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- vocabulary size: 32768 |
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Every model uses its own tokenizer trained on language-specific HPLT data. |
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See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf). |
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[The training code](https://github.com/hplt-project/HPLT-WP4). |
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[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn) |
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## Example usage |
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This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`. |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_id") |
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model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_id", trust_remote_code=True) |
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mask_id = tokenizer.convert_tokens_to_ids("[MASK]") |
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input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt") |
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output_p = model(**input_text) |
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output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids) |
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# should output: '[CLS] It's a beautiful place.[SEP]' |
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print(tokenizer.decode(output_text[0].tolist())) |
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``` |
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The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`. |
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## Intermediate checkpoints |
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We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`. |
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You can load a specific model revision with `transformers` using the argument `revision`: |
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```python |
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model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_id", revision="step21875", trust_remote_code=True) |
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``` |
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You can access all the revisions for the models with the following code: |
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```python |
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from huggingface_hub import list_repo_refs |
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out = list_repo_refs("HPLT/hplt_bert_base_id") |
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print([b.name for b in out.branches]) |
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``` |
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## Cite us |
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```bibtex |
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@inproceedings{samuel-etal-2023-trained, |
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title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus", |
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author = "Samuel, David and |
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Kutuzov, Andrey and |
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{\O}vrelid, Lilja and |
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Velldal, Erik", |
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editor = "Vlachos, Andreas and |
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Augenstein, Isabelle", |
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booktitle = "Findings of the Association for Computational Linguistics: EACL 2023", |
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month = may, |
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year = "2023", |
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address = "Dubrovnik, Croatia", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.findings-eacl.146", |
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doi = "10.18653/v1/2023.findings-eacl.146", |
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pages = "1954--1974" |
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}) |
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``` |
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```bibtex |
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@inproceedings{de-gibert-etal-2024-new-massive, |
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title = "A New Massive Multilingual Dataset for High-Performance Language Technologies", |
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author = {de Gibert, Ona and |
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Nail, Graeme and |
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Arefyev, Nikolay and |
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Ba{\~n}{\'o}n, Marta and |
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van der Linde, Jelmer and |
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Ji, Shaoxiong and |
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Zaragoza-Bernabeu, Jaume and |
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Aulamo, Mikko and |
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Ram{\'\i}rez-S{\'a}nchez, Gema and |
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Kutuzov, Andrey and |
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Pyysalo, Sampo and |
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Oepen, Stephan and |
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Tiedemann, J{\"o}rg}, |
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editor = "Calzolari, Nicoletta and |
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Kan, Min-Yen and |
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Hoste, Veronique and |
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Lenci, Alessandro and |
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Sakti, Sakriani and |
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Xue, Nianwen", |
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booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", |
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month = may, |
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year = "2024", |
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address = "Torino, Italia", |
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publisher = "ELRA and ICCL", |
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url = "https://aclanthology.org/2024.lrec-main.100", |
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pages = "1116--1128", |
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abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.", |
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} |
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``` |
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